Description Usage Arguments Details Value References

`ADASYN`

over-samples the input data using the Adaptive Synthetic
Sampling algorithm.

1 |

`data` |
A data frame containing the predictors and the outcome. The
predictors must be numeric and the outcome must be both a binary valued
factor and the last column of |

`perc_min` |
The desired % size of the minority class relative to the
whole data set. For instance, if |

`perc_over` |
% of examples to append to the input data set relative
to the size of the minority class. For instance, if |

`k` |
Number of nearest neighbours to compute for each example in the minority class. |

`classes` |
A named vector identifying the majority and the minority classes. The names must be "Majority" and "Minority". This argument is only useful if the function is called inside another sampling function. |

ADASYN is an adaptation of the SMOTE algorithm which focuses on
synthesising more examples for the minority examples that are considered
"hard" to learn. The learning hardness of a minority example is defined as
being proportional to the number of majority examples among the `k`

nearest neighbours of the minority example. There are two cases where
no examples are synthesised for a minority example. The first case is when
all `k`

nearest neighbours belong to the majority class and the
minority examples is considered to be noise. The second case is when all
`k`

nearest neighbours belong to the minority class and the minority
example is considered too easy to learn (learning hardness = 0).

Compared to ADASYN's original description, the current implementation has
a few differences. Firstly, the *d_{th}* parameter was dropped.
Secondly, the *β* parameter was replaced by `perc_min`

and
`perc_over`

parameters. The modification allows the user to synthesise
as many examples as wanted and *β = 1* is equivalent to
`perc_min`

= 50 (balance the distribution of examples).

A data frame containing a more balanced version of the input data set after over-sampling it with ADASYN.

He, H., Bai, Y., Garcia, E. A., & Li, S. (2008, June). ADASYN:
Adaptive synthetic sampling approach for imbalanced learning. In
*Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on
Computational Intelligence). IEEE International Joint Conference on*
(pp. 1322-1328). IEEE.

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